Predictive control is control that predicts future controlled quantities on the basis of a dynamic model of a process to determined operation quantities. Features of predictive control include easiness in achieving multivariable control, easiness in taking constraints into consideration, and easiness and intuitiveness in performing adjustment.
Predictive control has been used mainly for plant control in the petrochemical industry. With recent improvement in the performance of computers, predictive control has come to be applied to not only plant control in the petrochemical industry but also moving vehicles and robots, which have short control cycles (for example, refer to NPL 1).
General predictive control, including a technology disclosed in NPL 1, is based on the premise that a predictive-control model reflecting a real world is known, and a designer of the predictive-control model describes the predictive-control model in advance (for example, refer to PLT 1). A model prediction control device disclosed in PLT 1 has a known predictive-control model and, on the basis of an evaluation function, performs control that tracks the model. A predictive-control model is often given by a discrete time linear function.
Control devices that learn predictive-control models for control by using neural networks have been used (for example, refer to PLT 2). A control device disclosed in PLT 2 has an identification device that calculates parameters of an identification model (equivalent to the above-described predictive-control model). However, the control device disclosed in PLT 2 is also based on the premise that a predictive-control model is known.
Methods to learn parameters of a predictive-control model have been proposed (for example, refer to PLT 3). A method disclosed in PLT 3 is also based on the premise that a predictive-control model is known, and estimates parameters under the premise.
For power-assisted systems or the like, systems, which operate by selecting a control algorithm out of a plurality of fixed control algorithms to suit variation between individuals, have been proposed (for example, refer to PLT 4).
Devices using machine learning have also been proposed (for example, refer to PLT 5).